@inproceedings{177c290150d74dfe9b59163edccc628c,
title = "Reinforcement learning consensus control for discrete-time multi-agent systems",
abstract = "In this paper, the consensus control of leader-follower multi-agent systems is investigated. To achieve the consensus of the discrete-time multi-agent systems, the data-driven iterative neighbor and target Q-learning algorithm is proposed. To implement the proposed method, the actor-critic architecture with neighbor and target networks are employed to approximate the Q-function and control signal. The reasonable reinforcement signal and cost function are chosen from the environment. This method is independent on the accurate system model where most practical systems are too complicated to build the accurate models. Finally, the simulation example is given to demonstrate the effectiveness of the proposed approach.",
keywords = "Actor-Critic Networks, Consensus, Neighbor Networks, Reinforcement Learning, Target Networks",
author = "Xiaoxia Zhu and Xin Yuan and Yuanda Wang and Changyin Sun",
note = "Publisher Copyright: {\textcopyright} 2019 Technical Committee on Control Theory, Chinese Association of Automation.; 38th Chinese Control Conference, CCC 2019 ; Conference date: 27-07-2019 Through 30-07-2019",
year = "2019",
month = jul,
doi = "10.23919/ChiCC.2019.8865975",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "6178--6182",
editor = "Minyue Fu and Jian Sun",
booktitle = "Proceedings of the 38th Chinese Control Conference, CCC 2019",
}